Claude-Powered MCP Agent for Smart Supply Chain

This project simulates a smart warehouse system powered by Claude using Model Context Protocol (MCP) patterns. The system manages inventory, automated guided vehicles (AGVs), and order processing through a set of specialized agents coordinated by Claude.

Project Structure

claude-mcp-agent-for-supply-chain/
├── agents/                # MCP agent modules
├── simulation/            # Warehouse simulation logic
├── api/                   # FastAPI endpoints
├── logs/                  # Action and decision logs
├── claude_interface.py    # Interface to Claude API
├── main.py                # Main application entry point

Features

• MCP-style Modular Agents: InventoryManager, AGVPlanner, RestockAgent, Coordinator
• Warehouse Simulation: Inventory tracking, AGV movement, order processing
• Claude Integration: Uses Anthropic's Claude API for decision-making
• API Endpoints: FastAPI-based endpoints for interacting with the system

Setup

1. Create a virtual environment:

   python -m venv venv
   

2. Activate the virtual environment:

   • Windows: venv\Scripts\activate
   • Unix/MacOS: source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt
   

4. Set up environment variables:

   cp claude.env.template claude.env
   

   Then edit claude.env to add your Anthropic API key.

5. Run the application:

   python main.py
   

API Endpoints

• GET /inventory: Get current inventory status
• GET /agvs: Get status of all AGVs
• POST /orders: Create a new order
• POST /ask-agent: Send a query to Claude agent
• GET /logs: Get recent action logs

Example Usage

Example prompt to Claude:

The inventory for Product X is at 5 units, below the threshold of 10. Two AGVs are available. Suggest an optimal action.

Claude will analyze the situation and return structured actions that the system can execute.

License

MIT